Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Semantic Annotation of Sports Videos
IEEE MultiMedia
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
Highlight ranking for sports video browsing
Proceedings of the 13th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Trajectory based event tactics analysis in broadcast sports video
Proceedings of the 15th international conference on Multimedia
Personalized abstraction of broadcasted American football video by highlight selection
IEEE Transactions on Multimedia
Human Behavior Analysis for Highlight Ranking in Broadcast Racket Sports Video
IEEE Transactions on Multimedia
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Most of existing work on sports video analysis concentrates on highlight extraction. Few efforts devoted to the important issue as how to organize the extracted highlights which is adapt for the user preference. In this paper, we propose a novel approach to rank the highlights extracted from broadcast tennis video based on multi-modality analysis and relevance feedback. Firstly, visual and auditory features are employed to construct the mid-level representations for the content of broadcast tennis video. Then, the affective features are extracted from mid-level representations and the multiple ranking models are built using nonlinear regression algorithm. Finally, the ranking models are linearly combined to generate the final highlight ranking results. The relevance feedback technique is employed to effectively capture the user interest in visual and auditory attention spaces to adjust the ranking results being suitable to the user preference. The experimental results are encouraging and demonstrate that our approach is effective.